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Joseph Romano, PhD, MPhil, MA is an Assistant Professor of Informatics in the Department of Biostatistics, Epidemiology and Informatics, Senior Fellow in the Institute for Biomedical Informatics (IBI) and an investigator in the Center of Excellence in Environmental Toxicology (CEET).

Dr. Xie’s current research goals are twofold. First, she continues to develop new statistical methods for survival analysis, missing data, measurement error problems, high dimensional data, biomarker evaluations, and longitudinal […]

Dr. Zheng is an assistant professor of Biostatistics. His research focuses on the development of novel statistical methods to address important scientific problems, and the application of sound statistical approaches […]

Dr. Shen is a Professor and the Deputy Director of the Informatics Division in the Department of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine in the University […]

Dr. Shou’s methodological research focuses mainly on developing novel functional data analysis and machine learning models for multimodal and longitudinal imaging measures, wearable computing sensor data, and assessing biomarker reproducibility. […]

Dr. Wagenaar’s research is focused on the mechanisms of data integration, analysis and sustainable scientific collaboration through technology. The challenges of integrating complex scientific data are accompanied by a need […]

Joseph D. Romano is an Assistant Professor of Informatics and Pharmacology in the University of Pennsylvania’s Perelman School of Medicine. He joined the faculty in 2023 after completing postdoctoral and […]

Dr. Linn’s methodological research addresses barriers to incorporating complex, high-dimensional data into models for personalized medicine and biomarker development using neuroimaging data. As a co-investigator, she provides biostatistical leadership on […]

Dr. Long’s research purposefully includes novel statistical and machine learning research and impactful biomedical research, each of which reinforces the other. Its thrust is to develop statistical and machine learning […]

In addition to his professorial role, Dr. Morris serves as Director of the Division of Biostatistics. His research interests focus on developing quantitative methods to extract knowledge from biomedical big […]

Dr. Danielle Mowery is a collaborative investigator that develops natural language processing (NLP) solutions for processing clinical texts – i.e., clinical notes, chatbots, and transcribed texts – to support clinical […]

Dr. Huang’s research focuses on methodology development to understand the dynamics of disease activities and inform health management using multivariate longitudinal health data. She is currently the PI of a […]

Dr. Jin’s research focuses on developing statistical methods and computational tools to address cutting-edge problems in public health and medicine by integrating large-scale, multi-source datasets. Her research areas include health […]

Dr. Dokyoon Kim is an Associate Professor of Informatics and serves as the Director of the Center for AI-Driven Translational Informatics (CATI). He also holds the role of Associate Director […]

Dr. Getz is a pediatric cancer epidemiologist whose research aims to improve outcomes for children and young adults with cancer. Her research has focused on understanding the occurrence and subsequent […]

Dr. Himes’ work focuses on using biomedical informatics approaches to study asthma and other complex traits. Dr. Himes began asthma genetics and pharmacogenetics research by participating in genome-wide association studies […]

John H. Holmes, PhD, is Professor of Medical Informatics in Epidemiology at the University of Pennsylvania Perelman School of Medicine. He is the Associate Director for Medical Informatics at the […]

Dr. Yong Chen is Professor of Biostatistics at Department of Biostatistics, Epidemiology, and Informatics (DBEI), and a Senior Scholar at Center for Clinical Epidemiology & Biostatistics (CCEB) at the University […]

Dr. Demiris is the Mary Alice Bennett University Professor in the School of Nursing and Perelman School of Medicine, and Associate Dean for Research and Innovation in the School of […]

Masking policies during Covid-19 have inspired plenty of political debate, but scientific evidence about the policies’ effects has been very limited. This isn’t surprising, given that it is not practical […]

To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi-site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple […]

While many findings in neuroimaging studies pertain to multiple imaging modalities, statistical methods underlying intermodal comparisons have varied. In our recently published paper in Human Brain Mapping, we propose the […]

While aggregation of neuroimaging datasets from multiple sites and scanners can yield increased statistical power, it also presents challenges due to systematic scanner effects. This unwanted technical variability can introduce […]

NEXT SEMINAR Time: Friday, May 14, 1:30pm-3pm EST Topic: Broader and deeper engagement in biomedical data science Moderator: Dr. Hongzhe Li, Vice Chair of Research Integration, DBEI Our panelists are: […]

The field of neuroimaging dedicated to mapping connections in the brain is increasingly being recognized as key for understanding neurodevelopment and pathology. Networks of these connections are quantitatively represented using […]

The central vein sign is a promising MR imaging diagnostic biomarker for multiple sclerosis. Recent studies have demonstrated that patients with MS have higher proportions of white matter lesions with […]

As the field of neuroimaging grows, it can be difficult for scientists within the field to gain and maintain a detailed understanding of its ever-changing landscape. While collaboration and citation […]

Estimating scaled treatment effects with multiple outcomes Edward H Kennedy, Shreya Kangovi, Nandita Mitra Link In classical study designs, the aim is often to learn about the effects of a […]

Sensitivity analysis and power for instrumental variable studies Wang X, Jiang Y, Zhang NR, Small DS. Link In observational studies to estimate treatment effects, unmeasured confounding is often a concern. […]

A general approach to evaluating the bias of 2‐stage instrumental variable estimators Fei Wan, Dylan Small, and Nandita Mitra Unmeasured confounding is a common concern when researchers attempt to estimate […]

Bayesian nonparametric generative models for causal inference with missing at random covariates Jason Roy, Kirsten J. Lum, Bret Zeldow , Jordan D. Dworkin, Vincent Lo Re III, and Michael J. […]

Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WML) in multiple sclerosis. While WML have been studied for over two decades using […]

With the proliferation of multi-site neuroimaging studies, there is a greater need for handling non-biological variance introduced by differences in MRI scanners and acquisition protocols. Such unwanted sources of variation, […]

Diffusion tensor imaging (DTI) is a well-established magnetic resonance imaging (MRI) technique used for studying microstructural changes in the white matter. As with many other imaging modalities, DTI images suffer […]

MR imaging can be used to measure structural changes in the brains of individuals with multiple sclerosis and is essential for diagnosis, longitudinal monitoring, and therapy evaluation. The North American […]

A doubly robust approach for cost–effectiveness estimation from observational data Li, Vachani, Epstein, Mitra Estimation of common cost–effectiveness measures, including the incremental cost–effectiveness ratio and the net monetary benefit, is […]

A framework for Bayesian nonparametric inference for causal effects of mediation. Kim C, Daniels MJ, Marcus BH, Roy JA We propose a Bayesian non-parametric (BNP) framework for estimating causal effects […]

A Bayesian nonparametric approach to marginal structural models for point treatments and a continuous or survival outcome. Roy J, Lum KJ, Daniels MJ. Marginal structural models (MSMs) are a general […]